The principal uncertainties in climate predictions arise from the
representation of unresolvable yet important small-scale processes,
such as those controlling cloud cover [1]}, [2]}, [3]}, [4]}, [5]}, [6]}, [7]}, [8]}, [9]}.
These processes are represented by parameterization schemes,
which relate unresolved quantities such as cloud
statistics to variables resolved on the climate models' computational
grid, such as temperature and humidity. The parameterization
schemes depend on parameters that are a priori unknown, and so fixing the parameters is associated with uncertainty. The process of fixing these parameters to values that are most consistent with data is known as calibration, which generally requires solving an optimization problem. Traditionally, however, parameters are calibrated (“tuned”) by hand, in a process that exploits only a small subset of the available observational data and relies on the knowledge and intuition of climate modelers about plausible ranges of parameters and their effect on the simulated climate of a model [10]}, [11]}, [12]}, [13]}, [14]}, [15]}, [16]}, [17]}. More recently, some broader-scale automated approaches that more systematically quantify the plausible range of parameters have begun to be explored [18]}, [19]}.
